Evidence map›Paper›PMID 38609917›Full record

ArticleBMC cancer2024

A chest CT-based nomogram for predicting survival in acute myeloid leukemia.

Xiaoping Yi, Huien Zhan, Jun Lyu, Juan Du, Min Dai, Min Zhao, Yu Zhang, Cheng Zhou, Xin Xu, Yi Fan and 13 more

Erratum issuedOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.1field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors at 9 institutions in 1 country.

Xiaoping Yi *Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Huien Zhan *Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Jun Lyu *Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Juan Du *Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Min DaiDepartment of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Min ZhaoDepartment of Nuclear Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Yu ZhangDepartment of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Cheng ZhouDepartment of Hematology, Xiangya Hospital, Central South University, Changsha, China.
Xin XuDepartment of Geriatrics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Yi FanDepartment of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Lin LiDepartment of Hematology, Hunan Provincial People' Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
Baoxia DongDepartment of Hematology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai,, China.
Xinya JiangDepartment of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Zeyu XiaoThe Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Jihao ZhouDepartment of Hematology, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.
Minyi ZhaoDepartment of Hematology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
Jian ZhangDepartment of Hematology, The Third Xiangya hospital, Central South University, Changsha, China.
Yan FuDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Tingting ChenDepartment of Hematology, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.
Yang XuDepartment of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Jie TianCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. tian@ieee.org.
Qifa LiuDepartment of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China. liuqifa628@163.com.
Hui ZengDepartment of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China. androps2011@hotmail.com.
Central South University · CNFirst Affiliated Hospital of Jinan University · CNNanfang Hospital · CNJinan University · CNSoochow University · CNChinese Academy of Sciences · CNGuangzhou First People's Hospital · CNHunan Normal University · CNShanghai First People's Hospital · CN

Funding

China Postdoctoral Science Foundation 2022M713536National Natural Science Foundation of China 81970143National Natural Science Foundation of China U22A20377Natural Science Foundation of Guangdong Province 2021B1515020017Natural Science Foundation of Hunan Province 2022JJ30979
6 · The paper itself

Abstract

backgroundThe identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML).

methods952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements.

resultsThree CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011).

conclusionsIn summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Indexed as

Leukemia, Myeloid, AcuteNomogramsArea Under CurveHumansRetrospective StudiesTomography, X-Ray ComputedAcute myeloid leukemiaChest computed tomographyCT-derived featuresNomogramPrognosis

Identifiers

PMID38609917
PMCPMC11010287
OpenAlexW4394753567

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.